Build your real-time personalized recommender from scratch!

We just released a free course on building an H&M real-time personalized recommender.

The 5-lesson course has written tutorials, notebooks, Python code, and cloud deployments.

.

> What you'll learn

The course will focus on engineering a production-ready recommender system touching:

- ML system design

- the architecture of a real-time personalized recommender

- MLOps best practices (feature store, model registry, serving)

> What you'll build

- A production-ready recommender system with real-time personalization

- 4-stage recommender architecture for instant recommendations

- Two-tower model for user and fashion item embeddings

- LLM-enhanced recommendation system

> Tech stack & deployment

- Feature engineering with Polars

- Real-time serving with KServe

- MLOps infrastructure using the Hopsworks AI Lakehouse

- Offline batch deployments using GitHub Actions

- Interactive UI with Streamlit Cloud

- dependency management using 'uv'

The outcome will be an H&M deployed personalized recommender you can tinker with.

.

I've learned a lot from my past open-source courses.

Together with Hopsworks, we've made something special.

💻 Access the code and lessons: github.com/decodingml/h…

#machinelearning #artificialintelligence #mlops